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Data analytics is fundamental to several corporate functions, such as manufacturing, marketing and customer service. Its growing role in strategic decision-making, however, is less explored. Yet, research (for example, see this study by MIT researchers Andrew McAfee and Erik Brynjolfsson) finds that companies that introduce big data and analytics in their strategic decision-making benefit from increased productivity and pro­fitability rates that are 5% to 6% higher than those of their peers. Therefore, the strategy toolkit of organisational leaders that largely emphasises mindsets, courage and visionary leadership needs to be expanded to include data and analytical decision-making. Below, we explore the case for analytics in strategy.

Beat the Bias in Decision-Making

A vast body of literature exists about how managers make decisions, especially in a world of uncertainty. Behavioural researchers have found that our decision-making process, especially in times of discontinuous change, is impeded by relying on our intuition, heuristics, and rules of thumb or mental “shortcuts” to make judgments, resulting in distorted choices that merely confirm our biases. On the other hand, analytical decision-making that is based on evidence and data serves organisations better in terms of greater preparedness for change, business pivots and innovation, especially in times of discontinuous change.  

The sub-optimal nature of heuristic-based decision-making under uncertainty and ambiguity may be explained by cognitive biases that intuition (the “inside view”) is subject to that distort effective strategy formulation. Managers, over decades of experience, get caught in competency traps, whereby their lens on any new situation is driven by their prior experience and competence that may be misaligned with a new or rapidly evolving business environment. Resultant cognitive biases lead managers to formulate flawed strategies in uncertain times (e.g., the pandemic).

Cognitive biases also impact strategy execution. A salient bias is “planning fallacy”, i.e., when even experienced managers underestimate the amount of time and money needed to complete tasks due to being overly influenced, confident and optimistic from successful past experiences. The planning fallacy was first proposed by Nobel laureate Daniel Kahneman and his research partner Amos Tversky. They argued for the case that external data analytics can be leveraged to make better forecasts by building a statistical view of projects based on a reference class of similar projects. This corrective procedure of taking the “outside view” through data-driven decisions, can increase the odds of success of strategy execution and keep overoptimism at bay.

In general, data and analytics present an opportunity to bring more science to strategy development. In the 1970s, music conglomerate EMI made a bold move and entered the medical diagnostics market with the world’s first computed CT scanner. However, although EMI’s medical innovation was widely lauded, the company incurred losses and eventually left the market. Given the market was very different from the one it operated in, its pursuit required an “outside” perspective rather than a myopic, “inside view” that EMI relied on. The company failed to identify schools of leadership and reference class of organisations to learn from, thereby, miscalculating its ability to survive in this new market where other competitors entered the fray.

Identify Growth Opportunities and Complex Market Dynamics

Advanced analytics is critical to identifying new growth opportunities that represent a discontinuity from the current business and could, perhaps, eclipse competitors. The more nimble and savvy companies embrace ground-breaking advanced analytics models to identify new industry segments and acquisition targets. They are able to make bold forays into new product segments due to algorithms that use sophisticated network analysis and natural-language processing to parse complex data. For example, AI computational algorithms aid biopharma companies identify new patient segments and provide strategic direction to understanding changing customer behaviour.

Machine learning engines comb through massive documents of unstructured data, such as patent filings and clinical-trials reports, to extract meaningful insights that enable improved decision-making. By finding patterns in disparate data sources (e.g., through geography-led consumer behaviour), the technology points to emerging industry trends that will add value to the management’s strategy process.

Companies seeking to leverage advanced analytics to develop customer-focused strategies can use real-time tools to perform ‘sentiment analysis’. This opens up a new realm of possibilities for organisations on a mission to understand what consumers think of their brand.

Compete With a New Class of Competitors

Organisations today, across sectors, compete with a new class of competitors, notably, technology startups and technology giants. These companies and their business models, in contrast to the principles of competition of the industrial economy such as supply side economies of scale, resource ownership, internal optimisation, and consumer value, focus on demand side economies of scale, resource orchestration, external interaction, and ecosystem value. In turn, this new class of competition uses data and analytics extensively to drive strategy. Consider that several of Amazon’s in-house brands, be it Basics that accounts for an estimated third of all digital sales in batteries or Elements that holds over 15% of online market share in baby wipes, have emerged product leaders within a few years of their inception. Amazon’s data on the demand for various products in its marketplace allows it to not only identify relatively brand-agnostic product categories that it could potentially enter, but also the position it must occupy in these categories, towards category leadership. Competing with the digital economy players and creating competitive values requires firms to treat data as a key competitive asset and integrate analytics with business strategy to drive innovation and growth.

A Glimpse Into a ‘Strategic’ Future

A McKinsey study of dozens of companies found that to fully exploit data and analytics, organisations require three capabilities. First, they must be able to identify, combine, and manage multiple sources of data. Second, they need to build advanced analytics models for predicting and optimising organisational outcomes. Third, the management should strive to transform the organisation in terms of structures, processes and culture so that the data results in quality decision-making processes. While the onus is on senior managers to tap into new opportunities using advanced analytics tools, they have another, bigger role to play. They must empower employees—from the front lines to the C-Suite—to integrate data with strategy. By doing so, they will prove that (wo)man and machine can work in tandem to drive analytical strategies that create significant competitive value in tomorrow’s businesses.

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